With the increased popularity and consumer trust of online transactions, more and more consumers are doing business over the internet from their computers. As a result of any transaction over the internet, information is created in the form of data that characterizes the transaction, the transaction participants, and many of the circumstances and conditions surrounding the transaction. It has become customary to record and study this transaction data for the benefit of future business decisions and future internet offerings in general. However, with the increasing number of internet users worldwide the data generated by these network interactions is massive and voluminous. This data must be organized in order for it to be useful to businesses and consumers alike. Advances in database creation and management have provided opportunities for data aggregation and more timely use of accumulated data.
Typically, a database user will decide the attributes that they believe are useful for a given study. The database user will form a table of user attributes, transaction attributes, and product attributes that they believe are relevant to the study. They will then begin to collect values for storage within the table. Often times the data that is available is already in a table that has been collected prior to the conception of the study. Large entities or businesses maintain databases. Over time these databases become very large. Such entities and businesses may have maintained such databases for many years and the data contained therein can be studied after the fact at a later date.
Data from various sources may be added to these large databases to help retailers understand the needs and wants of customers. For example, data sources such as Enterprise Data, Social Data, Mobile Data along with Online Data may be added the data sources to understand the 360° of a customer. These additional data sources bring both structured and unstructured data along with very large volumes.
Further, it will be appreciated that retailers are operating across multiple channels. Retailers are making an effort to understand the customer across these channels and provide seamless interaction for customers across these channels. This aspect of omni-channel retailing along with need for personalization of the offers calls for analyzing data from multiple sources and banners calls for leveraging Hadoop based systems for analyzing massive volume of data efficiently.
What is needed are methods, systems, and computer program products for generating attribute tables for holding attributes while a corresponding business plan is in an approval process; methods, systems and computer program products for approving proposed business plans and automatically generating workflow for establishing data tables for aggregating customer profile data in those data tables; and methods, systems, and computer program products for linking customer attributes to perform analytics on customer behavior. As will be seen, the disclosure provides such methods, systems and computer programs for generating attribute tables for holding attributes while a corresponding business plan is in an approval process, for approving proposed business plans and automatically generating workflow for establishing data tables for aggregating customer profile data in those data tables, and for linking customer attributes to perform analytics on customer behavior in an effective and elegant manner.